Learning objectives
Knowledge and understanding: the course aims to provide the students with the theoretical knowledge essential to the understanding of data and information derived from bioinformatics analyses.
Applying knowledge and understanding: through practical exercises under the guidance of the teacher, students will have the opportunity to acquire the skills necessary to perform analysis of biological data through the use of computers.
Making judgments: During the course, students will be trained to interpret the results of computer analysis and to assess the degree of reliability of the evidence bioinformatics.
Communication skills: through examples, students will learn how to communicate in a written paper (consisting of text and images) results and interpretations of bioinformatics analysis.
Prerequisites
Basic knowledge of biological macromolecules and the foundations of Biochemistry and Molecular Biology. Skills in the use of computer and browsing the Internet.
Course unit content
Teaching of Bioinformatics (6 credits).
The first part of the course will address the molecular mechanisms most relevant to an understanding of the bioinformatics analysis of DNA and proteins, with particular reference to the mode of evolution of molecules and biological information.
Thereafter, the operating principles of the main techniques of bioinformatics analysis will be discussed, such as the comparison of sequences, the homology search, phylogenetic analysis and prediction of structure and function.
The course will also address the bioinformatics techniques for the analysis of biological data produced on a large scale, with particular reference to the interpretation of information contained in the complete genomes.
Full programme
Topics:
Introduction to biological sequences and structures, management and analysis of data, the central dogma, the evolutionary history of the sequences, genome. Evolution of DNA and proteins: Neutral theory, homology, orthology, paralogy, similarity metrics for comparing sequences, PAM, divergence, molecular clock, accelerated evolution convergent evolution.
Biochemical predictions: biochemical properties, patterns and signals, convergent evolution of patterns, Prosite, looking for patterns, degradation signals: PEST, protein sorting, signal peptide, anchor sequences, glycosylation, phosphorylation, ProtParam.
Structure of RNA and proteins: Types of RNA secondary structures, hairpin, bulge loops, pseudoknots, Minimum free energy, stacking energy, covariance analysis, prediction of secondary structure, solvent accessibility, classes, folds and architectures, homology modeling , threading, Coiled coils, membrane proteins, transmembrane topology.
Pairwise alignment: alignment, combinatorial Dot plots, repeated sequences, algorithm, dynamic programming, Needleman-Wunsh, Smith-Waterman, significance.
Multiple Alignment: uses of multiple, progressive alignment, iterative alignment, profiles, hidden Markov models, Pfam, Sequence logos.
Databases and homology search: entry, GenBank, SwissProt, PDB, Expressed Sequence Tags, IMAGE, SRS, Entrez, FASTA, BLAST, PSI-BLAST, significance, sensitivity, selectivity, coverage.
Phylogenetic analysis: tree of life, the nomenclature of phylogenetic trees, cladograms, phylograms, ultrametric trees, rooted and unrooted trees, amino acid and nucleotide distances, UPGMA, Neighbour-joining, maximum likelihood, parsimony bootstrap.
Biological networks: random networks and regular networks, clustering coefficient, average path length, "small world" networks, scale free network. Types of data represented by biological networks.
Genomes: physical maps and genetic maps, DNA fingerprinting, BAC genomic sequencing methods: “clone by clone” and WGS assembly, contigs, scaffolds, draft and finished sequences, ORF, gene-finding. Comparative genomics. Functional associations between proteins inferred from complete genomes.
Bibliography
Bioinformatica, Pascarella e Paiardini, Zanichelli, 2010
Bioinformatics: Sequence and Genome analysis. D. W. Mount, CSHL Press, 2001
Protein Evolution. L. Patty, Blackwell Science, 1999
Teaching methods
In the theoretical lessons students will learn the foundations of its bioinformatics to biological problems. The principles of computational analysis of DNA sequences and proteins and the functioning of the main software programs used in bioinformatics will be discussed. Particular emphasis will be given to the biological significance of computational analyses, with an illustration of the processes and molecular mechanisms most relevant to bioinformatics.
The lectures will be alternated with practical exercises in a computer room. In the tutorials, students will be trained in the use of bioinformatics techniques for the solution of biological problems. Some exercises will be carried out together with the teacher; others will be presented as problems that students will face on their own using the tools provided in advance with the theoretical and practical lessons.
Blended learning with face-to-face and online teaching
(via Teams)
Assessment methods and criteria
During the practical exercises students will present written solutions to problems of sequence analyses. Dedicated software allows the teacher to check in real time such solutions.
At the end of the course it will be assigned to each student the analysis of a different portion genomic encoding an uncharacterized protein. Students will have to conduct such an analysis and submit a written report of the results obtained in the form of a scientific article.
The report will be assessed by the teacher and discussed during the oral examination. In particular, the teacher will evaluate the student's ability to apply knowledge of bioinformatics, the correctness of the interpretations and the ability to communicate the results. The oral examination will also be addressed to determine the student theoretical knowledge on the subject.
Other information
The course makes use of a classroom for the practical exercises equipped with 35 workstations connected to the Internet and equipped with the bioinformatics analysis programs discussed during the course
2030 agenda goals for sustainable development
goal 09 – industry, innovation and infrastructure
